# UXCascade: Scalable Usability Testing with Simulated User Agents

arXiv: https://arxiv.org/abs/2601.15777 · HTML: https://mlsanity.com/paper/2601.15777/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2026.

## Built on by (most cited first, 4 of 4)

- [Training Computer Use Agents to Assess the Usability of Graphical User Interfaces](https://mlsanity.com/paper/2604.26020/) (2026)
- [PerceptUI: LLM Agents as Human-Aligned Synthetic Users for UI/UX Evaluation](https://mlsanity.com/paper/2606.05697/) (2026)
- [From Subjective Judgments to Auditable Standards:Protocol-Guided AI Auditing of Website Redundancy](https://mlsanity.com/paper/2608.21476/) (2026)
- [Automatic multimodal UX improvement recommendations from LLM agent user simulations](https://mlsanity.com/paper/2609.22971/) (2026)

## Abstract (arXiv)

Simulated user agents are increasingly deployed in usability testing to support fast, iterative UX workflows, as they generate rich data such as action logs and think-aloud reasoning, but the unstructured nature of this output often obscures actionable insights. We present UXCascade, an interactive tool for extracting, aggregating, and presenting agent-generated usability feedback at scale. Our core contribution is a multi-level analysis workflow that (1) highlights patterns across persona traits, goals, and outcomes, (2) links agent reasoning to specific issues, and (3) supports actionable design improvements. UXCascade operationalizes this approach by listing agent goals, traits, and issues in a structured overview. Practitioners can explore detailed reasoning traces and annotated views, propose interface edits, and assess their impact across personas. This enables a top-down, exploration-driven analysis from patterns to concrete, actionable UX interventions. A user study with eight UX professionals demonstrates that UXCascade integrates into existing workflows, enabling iterative feedback during early-stage interface development.